Results 41 to 50 of about 1,274,940 (254)
Deep Network Representation Learning Method on Incomplete Information Networks [PDF]
Jisuanji kexue, 2021 The goal of network representation learning(NRL) is embedding network nodes into low-dimensional vector space,for effective feature representation of the downstream tasks.Due to the difficulty of information collection in the real-world scene-ries,large ...
FU Kun, ZHAO Xiao-meng, FU Zi-tong, GAO Jin-hui, MA Hao-ran
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Representation Learning for Scale-Free Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic ...
Feng, Rui +4 more
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Network representation learning: an overview [PDF]
SCIENTIA SINICA Informationis, 2017 Networks are important ways of representing objects and their relationships. A key problem in the study of networks is how to represent the network information properly. With the developments in machine learning, feature learning of network vertices has become an important area of study.
Cheng YANG +3 more
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Representation Learning for Attributed Multiplex Heterogeneous Network
, 2019 Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist
Bhagat Smriti +13 more
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Deep Representation Learning for Multimodal Brain Networks
, 2020 11 pages, 3 figures, MICCAI ...
Zhang, Wen +3 more
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Deep Inductive Network Representation Learning [PDF]
Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18, 2018 This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across-networks. In particular, DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where ...
Ryan A. Rossi +2 more
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Dynamic Influence Maximization via Network Representation Learning
Frontiers in Physics, 2022 Influence maximization is a hot research topic in the social computing field and has gained tremendous studies motivated by its wild application scenarios.
Wei Sheng +4 more
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Feature learning in feature-sample networks using multi-objective optimization
, 2017 Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly.
Tinós, Renato +2 more
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Learning network representations
The European Physical Journal Special Topics, 2017 In this review I present several representation learning methods, and discuss the latest advancements with emphasis in applications to network science. Representation learning is a set of techniques that has the goal of efficiently mapping data structures into convenient latent spaces. Either for dimensionality reduction or for gaining semantic content,
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Network Representation Learning: From Traditional Feature Learning to Deep Learning
IEEE Access, 2020 Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data.
Ke Sun +5 more
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